Biology subjects

Nash, M. A.

Publications and source records attributed to Nash, M. A..

6 recordsLinked to original sources

bioRxiv2026

Probing the scalability of ultra stable catch bond complexes

The catch bond complex between serine-aspartate repeat protein G (SdrG) from Staphylococcus epidermidis and the beta chain of fibrinogen (Fg{beta}) exhibits two distinct rupture populations when dissociated under tensile force. Such complexes present exciting possibilities for developing dynamic biomaterials due to their unique response to shear force. However, the environmental responsiveness of this complex and its influence on adhesion behaviour in multi-valent systems remain underexplored. Using AFM-single molecule force spectroscopy (AFM-SMFS) and spinning disk adhesion (SDA) assays, we examined how protein orientation, mutations, and environment influence the stability and scaling behaviour of this catch bond system. Our findings confirmed that anchor point location (i.e., the direction from which the protein is pulled) strongly influences catch bond behaviour, while an S338H mutation in the binding domain destabilised the interaction in both single-molecule and multi-valent adhesion assays. This research examines how catch bond behaviour translates from the nanoscale to microscale using single molecule and multi-valent cell adhesion measurements and provides a toolkit for exploiting catch bonds towards macroscale material applications.

biophysics↗

bioRxiv2025

Discovery of Electron Hole-hopping Redox Mutations in Myoglobin by Deep Mutational Learning

In addition to storing molecular oxygen, myoglobin catalyzes peroxidase-like reactions involving high valency iron(IV)-oxo species that support one-electron oxidations on a range of substrates at an open active site. In select metalloenzymes, long-range electron transfer can be mediated by hole-hopping pathways composed of aromatic residues that act as relay stations for oxidative equivalents. However, it remains unclear how sequence variations could introduce or alter such catalytic mechanisms in myoglobin. Here we used enzyme proximity sequencing (EP-Seq) to measure the peroxidase activity levels of >6,000 human myoglobin variants. The resulting fitness landscape reveals how aromatic substitutions, in particular surface-exposed tryptophans, can enhance peroxidase activity. Using protein language models in tandem with feedforward neural networks, we trained an accurate fitness predictor on the experimental dataset, and applied it to screen >4M double mutant variants. The predictions suggested a beneficial role for electron-hole-hopping mutations in improving peroxidase activity. We experimentally tested 20 high scoring variants in a yeast display assay, all of which outperformed wild type myoglobin. Three selected variants were also tested in soluble format and similarly showed improved performance. A focused combinatorial library yielded a top double tryptophan variant (Q92W/F107W) with 4.9-fold higher catalytic efficiency than wild type. These results show that deep mutational learning can identify myoglobin variants with enhanced peroxidase activity that are consistent with the involvement of hole-hopping pathways, with broad implications for biocatalyst and redox enzyme design.

bioengineering↗

bioRxiv2025

Enzyme-responsive Hemostatic Elastin-like Polypeptides for Fibrin Stabilization and Enhanced Coagulation in Thrombocytopenia

Hemorrhage remains a leading cause of mortality in trauma and surgery, and treatment options are limited for thrombocytopenic patients with impaired platelet function. Current plasma-derived hemostatic products face challenges including limited supply, storage requirements, and infectious risk. Here we report a recombinant protein-based hemostat designed to enhance clot mechanics through enzyme responsiveness and self-assembly, which integrates biophysical design principles with clot-targeted drug delivery. We rationally designed a library of enzyme-responsive glutamine (Q)-containing block elastin-like polypeptides (Q-block-ELPs) that reinforce fibrin clots through phase separation and covalent cross-linking. Q-block-ELPs incorporate glutamine residues within a peptide motif recognized by coagulation factor XIIIa, enabling site-specific grafting into fibrin networks during clot formation. By tuning polymer length, Q-block valency, and lower critical solution temperature (LCST) behavior, we engineered Q-block-ELPs to phase separate at body temperature and integrate into the fibrin architecture. In vitro, Q-block-ELPs increase fibrin network density and stiffness. In a thrombocytopenic mouse model, systemic administration reduced blood loss and accelerated clot formation. This strategy delivers a programmable, pathogen-free platform for systemic bleeding control, bridging biophysical protein design with translational hemostatic therapy, addressing an urgent need for platelet-deficient bleeding disorders.

bioengineering↗

bioRxiv2024

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.

biophysics↗

bioRxiv2021

Optimizing mechanostable anchor points of engineered lipocalin in complex with CTLA-4

We used single-molecule AFM force spectroscopy (AFM-SMFS) to screen residues along the backbone of a non-antibody protein binding scaffold (lipocalin/anticalin), and determine the optimal anchor point that maximizes binding strength of the interaction with its target (CTLA-4). By incorporating non-canonical amino acids into anticalin, and using click chemistry to attach an Fg{beta} peptide at internal sequence positions, we were able to mechanically dissociate anticalin from CTLA-4 by pulling from eight different anchoring residues using an AFM cantilever tip. We found that pulling on the anticalin from residue 60 or 87 resulted in significantly higher rupture forces and a decrease in koff by 2-3 orders of magnitude over a force range of 50-200 pN. Five of the six internal pulling points tested were significantly more stable than N- or C-terminal anchor points, rupturing at up to 250 pN at loading rates of 0.1-10 nN sec-1. Anisotropic network modelling and molecular dynamics simulations using the G[o]-MARTINI approach explained the mechanism underlying the geometric dependency of mechanostability. These results suggest that optimization of attachment residue position for therapeutic and diagnostic cargo can provide large improvements in binding strength, allowing affinity maturation without requiring genetic mutation of binding interface residues.

biophysics↗

bioRxiv2020

High Force Catch Bond Mechanism of Bacterial Adhesion in the Human Gut

Bacterial colonization of the human intestine requires firm adhesion of bacteria to insoluble targets under hydrodynamic flow. Here we report the molecular mechanism behind an mechanostable protein complex responsible for resisting high shear forces and adhering bacteria to cellulose fibers in the human gut. Using single-molecule force spectroscopy (SMFS), single-molecule FRET (smFRET), and molecular dynamics (MD) simulations, we resolved two binding modes and three unbinding reaction pathways of a mechanically ultrastable R. champanellensis (Rc) Dockerin-Cohesin (Doc-Coh) complex. The complex assembles in two discrete binding modes with significantly different mechanical properties, with one breaking at ~500 pN and the other at ~200 pN at loading rates from 1-100 nN/sec. A neighboring X-module domain allosterically regulates the binding interaction and inhibits one of the low-force pathways at high loading rates, giving rise to a new mechanism of catch bonding that manifests under force ramp protocols. Multi-state Monte Carlo simulations show strong agreement with experimental results, validating the proposed kinetic scheme. These results explain mechanistically how gut microbes regulate cell adhesion strength at high shear stress through intricate molecular mechanisms including dual-binding modes, mechanical allostery and catch bonds.

biophysics↗